A implementação do Projeto Intensivo no Ciclo (PIC) em Pompeia (SP): (re)construindo o processo formativo dos professores que ensinam Matemática

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Educação infantil: percepção de profissionais e familiares sobre inclusão, aprendizagem e desenvolvimento da criança com deficiência, em Maringá/BR e em Guadalajara/ES

Pós-graduação em Educação Escolar - FCLAR

Gestão democrática, educação e cidadania: um olhar crítico a partir de fontes bibliográficas

Pós-graduação em Educação Escolar - FCLAR

Inclusão: análise das práticas pedagógicas do ciclo alfabetização do ensino fundamental de escolas municipais de Limeira-SP

Pós-graduação em Educação - IBRC

Crítica ao conceito de Necessidades Básicas de Aprendizagem (NEBA) a partir da categoria marxiana de necessidades humanas

Pós-graduação em Educação Escolar - FCLAR

An Educational international partnership responding to local needs: Process evaluation of the Brazil FAIMER Regional Institute

The Brazilian public health system requires competent professionals sensitive to the needs of the population. The Foundation for Advancement of International Medical Education and Research (FAIMER) provides a two-year faculty development programme for health professions educators, aiming to build leadership in education to improve health. A partnership with governmental initiatives and FAIMER was established for meeting these needs. This paper describes the initial process evaluation results of the Brazilian FAIMER Institute Fellowship (FAIMER BR). Methods: Data were analysed for the classes 2007-2010 regarding: application processes; innovation project themes; retrospective post-pre self-ratings of knowledge acquisition; and professional development portfolios. Results: Seventeen of 26 Brazilian states were represented among 98 Fellows, predominantly from public medical schools (75.5%) and schools awarded Ministry of Health grants to align education with public health services (89.8%). One-third (n = 32) of Fellows' innovation projects were related to these grants. Significant increases occurred in all topic subscales on self-report of knowledge acquisition (eff ect sizes, 1.21-2.77). In the follow up questionnaire, 63% of Fellows reported that their projects were incorporated into the curriculum or institutional policies. The majority reported that the programme deepened their knowledge (98%), provided new ideas about medical education (90%) and provided skills for conflict management (63%). One-half of the Fellows reported sustained benefits from the programme listserv and other communications, including breadth of expertise, establishment of research collaboration and receiving emotional support. Conclusion: Contributors to initial programme success included alignment of curriculum with governmental initiatives, curriculum design merging educational technology, leadership and management skills and central role of an innovation educational project responding to local needs.

T-Learning: limites e possibilidades em televisão digital interativa

Pós-graduação em Televisão Digital: Informação e Conhecimento - FAAC

Semi-supervised learning for relevance feedback on image retrieval tasks

Relevance feedback approaches have been established as an important tool for interactive search, enabling users to express their needs. However, in view of the growth of multimedia collections available, the user efforts required by these methods tend to increase as well, demanding approaches for reducing the need of user interactions. In this context, this paper proposes a semi-supervised learning algorithm for relevance feedback to be used in image retrieval tasks. The proposed semi-supervised algorithm aims at using both supervised and unsupervised approaches simultaneously. While a supervised step is performed using the information collected from the user feedback, an unsupervised step exploits the intrinsic dataset structure, which is represented in terms of ranked lists of images. Several experiments were conducted for different image retrieval tasks involving shape, color, and texture descriptors and different datasets. The proposed approach was also evaluated on multimodal retrieval tasks, considering visual and textual descriptors. Experimental results demonstrate the effectiveness of the proposed approach.